L09 Themes

Data Visualization (STAT 302)

Overview

The goal of this lab is to play around with the theme options in ggplot2.

Datasets

We’ll be using the cdc.txt and NU_admission_data.csv datasets.

Code

# load package(s)
library(tidyverse)
library(gridExtra)
library(patchwork)

# read in the cdc dataset
cdc <- read_delim(file = "data/cdc.txt", delim = "|") %>%
  mutate(
    genhlth = factor(
      genhlth,
      levels = c("excellent", "very good", "good", "fair", "poor"),
      labels = c("Excellent", "Very Good", "Good", "Fair", "Poor")
      )
    )

# read in NU admission data
nu_data = read_csv("data/NU_admission_data.csv") %>% 
 janitor::clean_names()

# set seed
set.seed(2468)

# selecting a random subset of size 100
cdc_small <- cdc %>% 
  slice_sample(n = 100)

Exercise 1

Use the cdc_small dataset to explore several pre-set ggthemes. The code below constructs the familiar scatterplot of weight by height and stores it in plot_01. Display plot_01 to observe the default theme. Explore/apply, and display at least 7 other pre-set themes from the ggplot2 or ggthemes package. Don’t worry about making adjustments to the figures under the new themes. Just get a sense of what the themes are doing to the original figure plot_01.

There should be at least 8 plots for this task, plot_01 is pictured below. Use patchwork or cowplot in combination with R yaml chunk options fig-height and fig-width (out-width and fig-align may be useful as well) to setup the 8 plots together in a user friendly arrangement.

Code

# building plot
plot_01 <- ggplot(
  data = cdc_small,
  aes(x = height, y = weight)
  ) +
  geom_point(size = 3, aes(shape = genhlth, color = genhlth)) +
  scale_y_continuous(
    name = "Weight in Pounds",
    limits = c(100, 275),
    breaks = seq(100, 275, 25),
    trans = "log10",
    labels = scales::label_number(accuracy = 1, suffix = " lbs")
  ) +
  scale_x_continuous(
    name = "Height in Inches",
    limits = c(60, 80),
    breaks = seq(60, 80, 5),
    labels = scales::label_number(accuracy = 1, suffix = " in")
  ) +
  scale_shape_manual(
    name = "Health?",
    labels = c(
      "Excellent", "Very Good", "Good", "Fair", "Poor"
    ),
    values = c(17, 19, 15, 9, 4)
  ) +
  scale_color_brewer(
    name = "Health?",
    labels = c(
      "Excellent", "Very Good", "Good", "Fair", "Poor"
    ),
    palette = "Set1"
  ) +
  theme(
    legend.position = c(1, 0),
    legend.justification = c(1, 0)
  ) +
  labs(title = "CDC BRFSS: Weight by Height")

Solution

Code

legend_theme <- function(...){
  theme(
    legend.position = c(1, 0),
    legend.justification = c(1, 0)
  )
}
p1 <- plot_01 + ggtitle("Plot 1: Base") + legend_theme()
p2 <- plot_01 + theme_bw() + ggtitle("Plot 2: theme_bw") + legend_theme()
p3 <- plot_01 + theme_linedraw() + ggtitle("Plot 3: theme_linedraw") + legend_theme()
p4 <- plot_01 + theme_light() + ggtitle("Plot 4: theme_light") + legend_theme()
p5 <- plot_01 + theme_minimal() + ggtitle("Plot 5: theme_minimal") + legend_theme()
p6 <- plot_01 + theme_classic() + ggtitle("Plot 6: theme_classic") + legend_theme()
p7 <- plot_01 + theme_dark() + ggtitle("Plot 7: theme_dark") + legend_theme()
p8 <- plot_01 + theme_void() + ggtitle("Plot 8: theme_void") + legend_theme()

Code

p1 + p2 + p3 + p4 + p5 + p6 + p7 + p8 +
  plot_layout(nrow = 4, byrow = FALSE)

Important

Which theme or themes do you particularly like? Why?

Solution

I particularly like basic theme since it separates the plot and labels clearly with clear axies.

Exercise 2

Using plot_01 from Exercise 1 and the theme() function, attempt to construct the ugliest plot possible (example pictured below). Be creative! It should NOT look exactly like the example. Since the goal is to understand a variety of adjustments, you should use a minimum of 10 different manual adjustments within theme().

Solution

Code

plot_01 + 
    theme(
      legend.position = c(1, 0), 
      legend.justification = c(1, 0), 
      axis.title = element_text(color = "blue", face = "bold"), 
      axis.text = element_text(color = "red", angle = -90), 
      axis.ticks.length = unit(1, "in"), 
      panel.grid.major = element_line(color = "red", size = 1),
      panel.grid.minor = element_line(color = "red", size = 2, linetype = "dotted"), 
      panel.background = element_rect(fill = "lightblue"), 
      plot.background = element_rect(color = "green", fill = "pink", size = 10), 
      plot.subtitle = element_text(size = 20, family = "serif", face = "italic"),
      legend.background = element_rect(fill = "lemonchiffon", color = "grey50", size = 1, linetype = "dashed"))

Exercise 3

We will be making use of your code from Exercise 3 from L07 Layers. Using the NU_admission_data.csv you created two separate plots derived from the single plot depicted in undergraduate-admissions-statistics.pdf. Style these plots so they follow a “Northwestern” theme. You are welcome to display the plots separately OR design a layout that displays both together (likely one stacked above the other).

Check out the following webpages to help create your Northwestern theme:

• Visual Identity
• Color Palettes
• Fonts & Typography — Need to use substitute fonts

Additional requirement:

Use a free non-standard font from google for the title. Pick one that looks similar to a Northwestern font.

Note

I find this blog post to be extremely useful for adding fonts. Important packages for using non-standard fonts are showtext, extrafont, extrafontdb, and sysfonts. The last 3 generally just need to be installed (not loaded per session).

Solution

Code

bar_data = nu_data %>% 
  select(-contains("_rate")) %>% 
  pivot_longer(
    cols = -year,
    names_to = "category",
    values_to = "count"
  )

bar_label_data = bar_data %>% 
  mutate(
    col_label = prettyNum(count, big.mark = ",")
  )

# barplot
bar_plot = ggplot(data = bar_data, aes(x = year, y = count)) +
  geom_col(aes(fill = category), 
           position = "identity",
           width = 0.7) +
  geom_text(
    data = bar_label_data,
    mapping = aes(label = col_label),
    size = 1.5,
    color = "white",
    vjust = 1,
    nudge_y = -200
  ) +
  scale_x_continuous(
                     name = "Entering Year",
                     breaks = 1999:2020,
                     expand = c(0, 0.25)) +
  scale_y_continuous(name = "Applications",
                     expand = c(0, 0),
                     limits = c(0, 50000),
                     labels = scales::label_comma(),
                     breaks = seq(0, 50000, 5000)) +
  scale_fill_manual(
    name = NULL,
    limits = c("applications", "admitted_students", "matriculants"),
    labels = c("Applications", "Admitted Students", "Matriculants"),
    values = c("#B6ACD1", "#836EAA", "#4E2A84")
  ) +
  theme_classic() +
  theme(
    strip.text.x = element_text(family ="Georgia", face = "bold", size=13),
    legend.justification = c(0.5, 1),
    legend.position = c(0.5, 1),
    legend.direction = "horizontal",
    plot.title = element_text(family = "Georgia", color = "#716C6B", hjust = 0.5, size = 15)
  ) +
  ggtitle("Northwestern University\nUndergraduate Admissions 1999-2020")

bar_plot

Code

#ggsave("nu_bar_plot.pdf", bar_plot, width = 9, height = 4)

Code

rate_data = nu_data %>% 
  select(year, contains("_rate")) %>% 
  pivot_longer(
    cols = -year,
    names_to = "category",
    values_to = "value"
  ) %>% 
  mutate(
    rate_labels = paste0(value, "%"),
    label_y = case_when(
      category == "admission_rate" ~ value - 2,
      category == "yield_rate" ~ value + 2
    )
  )

#rate plot
rate_plot = ggplot(data = rate_data, 
                       aes(x = year, y = value)) +
  geom_line(aes(color = category)) +
  geom_point(
    mapping = aes(shape = category, fill = category),
    color = "white"
  ) +
  geom_text(
    mapping = aes(y = label_y, label = rate_labels),
    size = 2
  ) +
  scale_x_continuous(name = "Entering Year",
                     breaks = 1999:2020,
                     expand = c(0, 0.35)) +
  scale_y_continuous(name = NULL,
                     expand = c(0, 0),
                     limits = c(0, 70),
                     labels = scales::label_percent(scale = 1),
                     breaks = seq(0, 70, 10)) +
  scale_color_discrete(
    name = NULL,
    labels = c("Admission Rate", "Yield Rate")
  ) +
  scale_shape_manual(
    labels = c("Admission Rate", "Yield Rate"),
    name = NULL,
    values = c(21, 24)
  ) +
  scale_fill_discrete(name = NULL,
                      labels = c("Admission Rate", "Yield Rate")) +
  theme_classic() +
  theme(
    legend.justification = c(0.5, 1),
    legend.position = c(0.5, 1),
    legend.direction = "horizontal",
    plot.title = element_text(family = "serif", color = "#716C6B", hjust = 0.5, size = 15),
    legend.background = element_rect(color="#4E2A84", size = 1, linetype="dashed")
  ) +
  ggtitle("Northwestern University\nUndergraduate Admissions 1999-2020")

rate_plot

Code

bar_plot / (rate_plot + ggtitle(NULL))

Challenge

Challenge is optional for all students, but we recommend trying them out!

Using cdc_small dataset, re-create your own version inspired by the plot below.

Must haves:

• Use two non-standard fonts (one for labeling the point and the other for the axes)
• Use at least two colors (one for the added point, another for the rest of the points)
• A curved arrow used to label the point

Using Bilbo Baggins’ responses below to the CDC BRSFF questions, add Bilbo’s data point to a scatterplot of weight by height.

• genhlth - How would you rate your general health? fair
• exerany - Have you exercised in the past month? 1=yes
• hlthplan - Do you have some form of health coverage? 0=no
• smoke100 - Have you smoked at least 100 cigarettes in your life time? 1=yes
• height - height in inches: 46
• weight - weight in pounds: 120
• wtdesire - weight desired in pounds: 120
• age - in years: 45
• gender - m for males and f for females: m

Note

Adding non-standard fonts can be an adventure. I find this blog post to be extremely useful for adding fonts. Important packages for using non-standard fonts are showtext, extrafont, extrafontdb, and sysfonts. The last 3 generally just need to be installed (not loaded per session).

Hint:

• Create a new dataset (maybe call it bilbo or bilbo_baggins) using either data.frame() (base R - example in book) or tibble() (tidyverse - see help documentation for the function). Make sure to use variable names that exactly match cdc’s variable names. We have provided the tidyverse approach.
• Search google fonts to find some free fonts to use (can get free fonts from other locations)

Solution

Code

# build dataset for Bilbo Baggins
bilbo <- tibble(
  genhlth  = "fair",
  exerany  = 1,
  hlthplan = 0,
  smoke100 = 1,
  height   = 46,
  weight   = 120,
  wtdesire = 120,
  age      = 45,
  gender   = "m"
)